SoroushVahidi/scidocs huggingface dataset:
Biobanks: transnational, European and global networks.
(2007)

Biobanks contain biological samples and associated information that are essential raw materials for advancement of biotechnology, human health, and research and development in life sciences. Population-based and disease-oriented biobanks are major biobank formats to establish the disease relevance of human genes and provide opportunities to elucidate their interaction with environment and lifestyle. The developments in personalized medicine require molecular definition of new disease subentities and biomarkers for identification of relevant patient subgroups for drug development. These emerging demands can only be met if biobanks cooperate at the transnational or even global scale. Establishment of common standards and strategies to cope with the heterogeneous legal and ethical landscape in different countries are seen as major challenges for biobank networks. The Central Research Infrastructure for Molecular Pathology (CRIP), the concept for a pan-European Biobanking and Biomolecular Resources Research Infrastructure (BBMRI), and the Organization for Economic Co-operation and Development (OECD) global Biological Resources Centres network are examples for transnational, European and global biobank networks that are described in this article.
Implantable Antennas for Biomedical Applications: An Overview on Alternative Antenna Design Methods and Challenges
(2018)

Implanted biomedical devices are witnessing great attention in finding solutions to complex medical conditions. Many challenges face the design of implantable biomedical devices including designing and implanting antennas within hostile environment due to the surrounding tissues of human body. Implanted antennas must be compact in size, efficient, safe, and can effectively work within adequate medical frequency bands. This paper presents an overview of the major aspects related to the design and challenges of in body implanted antennas. The review includes surveying the applications, design methods, challenges, simulation tools, and testing and manufacturing of implantable biomedical antennas.
Neural networks for computer-aided diagnosis: detection of lung nodules in chest radiograms
(2003)

The paper describes a neural-network-based system for the computer aided detection of lung nodules in chest radiograms. Our approach is based on multiscale processing and artificial neural networks (ANNs). The problem of nodule detection is faced by using a two-stage architecture including: 1) an attention focusing subsystem that processes whole radiographs to locate possible nodular regions ensuring high sensitivity; 2) a validation subsystem that processes regions of interest to evaluate the likelihood of the presence of a nodule, so as to reduce false alarms and increase detection specificity. Biologically inspired filters (both LoG and Gabor kernels) are used to enhance salient image features. ANNs of the feedforward type are employed, which allow an efficient use of a priori knowledge about the shape of nodules, and the background structure. The images from the public JSRT database, including 247 radiograms, were used to build and test the system. We performed a further test by using a second private database with 65 radiograms collected and annotated at the Radiology Department of the University of Florence. Both data sets include nodule and nonnodule radiographs. The use of a public data set along with independent testing with a different image set makes the comparison with other systems easier and allows a deeper understanding of system behavior. Experimental results are described by ROC/FROC analysis. For the JSRT database, we observed that by varying sensitivity from 60 to 75% the number of false alarms per image lies in the range 4-10, while accuracy is in the range 95.7-98.0%. When the second data set was used comparable results were obtained. The observed system performances support the undertaking of system validation in clinical settings.
Particle Swarm Optimization of the Multioscillatory LQR for a Three-Phase Four-Wire Voltage-Source Inverter With an  $LC$ Output Filter
(2015)

This paper presents evolutionary optimization of the linear quadratic regulator (LQR) for a voltage-source inverter with an LC output filter. The procedure involves particle-swarm-based search for the best weighting factors in the quadratic cost function. It is common practice that the weights in the cost function are set using the guess-and-check method. However, it becomes quite challenging, and usually very time-consuming, if there are many auxiliary states added to the system. In order to immunize the system against unbalanced and nonlinear loads, oscillatory terms are incorporated into the control scheme, and this significantly increases the number of weights to be guessed. All controller gains are determined altogether in one LQR procedure call, and the originality reported here refers to evolutionary tuning of the weighting matrix. There is only one penalty factor to be set by the designer during the controller synthesis procedure. This coefficient enables shaping the dynamics of the closed-loop system by penalizing the dynamics of control signals instead of selecting individual weighting factors for augmented state vector components. Simulational tuning and experimental verification (the physical converter at the level of 21 kVA) are included.
Towards fabrication of Vertical Slit Field Effect Transistor (VeSFET) as new device for nano-scale CMOS technology
(2011)

This paper proposes a CMOS based process for Vertical Slit Field Effect Transistors. The central part of the device, namely, the vertical slit, is defined by using electron beam lithography and silicon dry etching. In order to verify the validity and the reproducibility of the process, devices having the slit width ranging from 16 nm to 400 nm were fabricated, with slit conductance in the range 0.6 to 3 milliSiemens, in agreement with the expected values.
Interpretable Counting for Visual Question Answering
(2018)

Questions that require counting a variety of objects in images remain a major challenge in visual question answering (VQA). The most common approaches to VQA involve either classifying answers based on fixed length representations of both the image and question or summing fractional counts estimated from each section of the image. In contrast, we treat counting as a sequential decision process and force our model to make discrete choices of what to count. Specifically, the model sequentially selects from detected objects and learns interactions between objects that influence subsequent selections. A distinction of our approach is its intuitive and interpretable output, as discrete counts are automatically grounded in the image. Furthermore, our method outperforms the state of the art architecture for VQA on multiple metrics that evaluate counting.
A systematic review of the impact of the use of social networking sites on body image and disordered eating outcomes.
(2016)

A large body of literature has demonstrated mass media effects on body image and disordered eating. More recently, research in this area has turned to 'new' forms of media, such as the Internet, and particularly Social Networking Sites (SNSs). A systematic search for peer-reviewed articles on SNS use and body image and eating disorders resulted in 20 studies meeting specific inclusion criteria. As a whole, these articles demonstrated that use of SNSs is associated with body image and disordered eating. Specific SNS activities, such as viewing and uploading photos and seeking negative feedback via status updates, were identified as particularly problematic. A small number of studies also addressed underlying processes and found that appearance-based social comparison mediated the relationship between SNS use and body image and eating concerns. Gender was not found to be a moderating factor. It was concluded that, although there is a good deal of correlational research supporting the maladaptive effect of SNS use on body image and disordered eating, more longitudinal and experimental studies are needed.
Supporting Cloud Computing in Thin-Client/Server Computing Model
(2010)

This paper addresses the issue of how to support cloud computing in thin-client/server computing model. We describe the design and implementation of the multiple-application-server thin-client/server computing (MAS TC/S) model that allows users with thin-client devices to roam around a wide area network whilst experiencing transparent working environments. MAS TC/S can be applied to a wide variety of applications in wide area network, such as pervasive software rental, service-oriented infrastructure for an Internet service provider, and office automation for transnational corporations. The MAS TC/S architecture model comprises five major components: the display protocol, the multiple-application-server topology, the application-server discovery protocol, the distributed file system, and the network input/output protocol. We discuss problems and present solutions in the design of each constituent component. A prototype of the MAS TC/S that spans the campuses of two universities in Taiwan has been built – we also report our experiences in constructing this prototype.
Explaining Inconsistencies in OWL Ontologies
(2009)

Justifications play a central role as the basis for explaining entailments in OWL ontologies. While techniques for computing justifications for entailments in consistent ontologies are theoretically and practically well-understood, little is known about the practicalities of computing justifications for inconsistent ontologies. This is despite the fact that justifications are important for repairing inconsistent ontologies, and can be used as a basis for paraconsistent reasoning. This paper presents algorithms, optimisations, and experiments in this area. Surprisingly, it turns out that justifications for inconsistent ontologies are more “difficult” to compute and are often more “numerous” than justifications for entailments in consistent ontologies: whereas it is always possible to compute some justifications, it is often not possible to compute all justifications for real world inconsistent ontologies.
Impact of interleaving on common-mode EMI filter weight reduction of paralleled three-phase voltage-source converters
(2013)

This paper presents a detailed analysis on the impact of the interleaving angle on EMI filter weight reduction in a paralleled three-phase VSI motor drive system. The EMI noise analysis equivalent circuits are given in frequency domain and EMI filter design methods for a motor drive system are discussed. Based on the analysis, design examples are given for DC and AC common-mode (CM) EMI filters showing that minimum corner frequency in this case cannot ensure minimum EMI filter weight. In effect, the EMI filter weight is also determined by the volt-seconds on the inductor. With this consideration, the impact of interleaving is analyzed based on the equivalent circuit developed. Specifically, it is shown that interleaving can either reduce the volt-second on the inductor or help reduce the inductance needed, which has a different impact on the filter weight when the design condition is different. EMI filters are designed for both AC and DC sides as examples, showing the impact that different interleaving angles can have on filter weight reduction. Verifications are carried out both in simulation and experimentally on a 2 kW converter system.
A hybrid RF and vibration energy harvester for wearable devices
(2018)

This paper describes a hybrid radio frequency (RF) and piezoelectric thin film polyvinylidene fluoride (PVDF) vibration energy harvester for wearable devices. By exploiting the impedance characteristics of parasitic capacitances and discrete inductors, the proposed harvester not only scavenges 15 Hz vibration energy but also works as a 915 MHz flexible silver-ink RF dipole antenna. In addition, an interface circuit including a 6-stage Dickson RF-to-DC converter and a diode bridge rectifier to convert the RF and vibration outputs of the hybrid harvester into DC signals to power resistive loads is evaluated. A maximum DC output power of 20.9 μ”, when using the RF to DC converter and −8 dBm input RF power, is achieved at 36 % of the open-circuit output voltage while the DC power harvested from 3 g vibration excitation reaches a maximum of 2.8 μW at 51% of open-circuit voltage. Experimental results show that the tested hybrid harvesting system simultaneously generates 7.3 μW DC power, when the distance from the harvester to a 3 W EIRP 915 MHz transmitter is 5.5 m, and 1.8 μW DC power from a 1.8 g vibration acceleration peak.
An Efficient End-to-End Neural Model for Handwritten Text Recognition
(2018)

Offline handwritten text recognition from images is an important problem for enterprises attempting to digitize large volumes of handmarked scanned documents/reports. Deep recurrent models such as Multi-dimensional LSTMs [10] have been shown to yield superior performance over traditional Hidden Markov Model based approaches that suffer from the Markov assumption and therefore lack the representational power of RNNs. In this paper we introduce a novel approach that combines a deep convolutional network with a recurrent Encoder-Decoder network to map an image to a sequence of characters corresponding to the text present in the image. The entire model is trained end-to-end using Focal Loss [18], an improvement over the standard Cross-Entropy loss that addresses the class imbalance problem, inherent to text recognition. To enhance the decoding capacity of the model, Beam Search algorithm is employed which searches for the best sequence out of a set of hypotheses based on a joint distribution of individual characters. Our model takes as input a downsampled version of the original image thereby making it both computationally and memory efficient. The experimental results were benchmarked against two publicly available datasets, IAM and RIMES. We surpass the state-of-the-art word level accuracy on the evaluation set of both datasets by 3.5% & 1.1%, respectively.
Supply chain information systems strategy : Impacts on supply chain performance and firm performance
(2015)

This paper examines the relationship between supply chain (SC) strategy and supply chain information systems (IS) strategy, and its impact on supply chain performance and firm performance. Theorizing from the supply chain and IS literatures within an overarching framework of the information processing theory (IPT), we develop hypotheses proposing a positive moderating effect of two supply chain IS strategies – IS for Efficiency and IS for Flexibility – on the respective relationships between two SC strategies – Lean and Agile, and supply chain performance. Based on confirmatory analysis and structural equation modeling of survey data from members of senior and executive management in the purchase/materials management/logistics/supply chain functions, from 205 firms, we validate these hypotheses and show that the IS for Efficiency (IS for Flexibility) IS strategy enhances the relationship between Lean (Agile) SC strategy and supply chain performance. We also show a positive association between supply chain performance and firm performance, and a full (partial) mediation effect of supply chain performance on the relation between Agile (Lean) SC strategy and firm performance. The paper contributes to the supply chain literature by providing theoretical understanding and empirical support of how SC strategies and IS strategies can work together to boost supply chain performance. In doing so, it identifies particular types of supply chain IS application portfolios that can enhance the benefits from specific SC strategies. The paper also develops and validates instruments for measuring two types of SC strategies and supply chain IS strategies. For practice, the paper offers guidance in making investment decisions for adopting and deploying IS appropriate to particular SC strategies and analyzing possible lack of alignment between applications that the firm deploys in its supply chain, and the information processing needs of its SC strategy. & 2012 Elsevier B.V. All rights reserved.
CIAN: Cross-Image Affinity Net for Weakly Supervised Semantic Segmentation
(2018)

Weakly supervised semantic segmentation based on image-level labels aims for alleviating the data scarcity problem by training with coarse labels. State-of-the-art methods rely on image-level labels to generate proxy segmentation masks, then train the segmentation network on these masks with various constraints. These methods consider each image independently and lack the exploration of cross-image relationships. We argue the cross-image relationship is vital to weakly supervised learning. We propose an end-to-end affinity module for explicitly modeling the relationship among a group of images. By means of this, one image can benefit from the complementary information from other images, and the supervision guidance can be shared in the group. The proposed method improves over the baseline with a large margin. Our method achieves 64.1% mIOU score on Pascal VOC 2012 validation set, and 64.7% mIOU score on test set, which is a new state-of-theart by only using image-level labels, demonstrating the effectiveness of the method.
Where2Stand: A Human Position Recommendation System for Souvenir Photography
(2015)

People often take photographs at tourist sites and these pictures usually have two main elements: a person in the foreground and scenery in the background. This type of “souvenir photo” is one of the most common photos clicked by tourists. Although algorithms that aid a user-photographer in taking a well-composed picture of a scene exist [Ni et al. 2013], few studies have addressed the issue of properly positioning human subjects in photographs. In photography, the common guidelines of composing portrait images exist. However, these rules usually do not consider the background scene. Therefore, in this article, we investigate human-scenery positional relationships and construct a photographic assistance system to optimize the position of human subjects in a given background scene, thereby assisting the user in capturing high-quality souvenir photos. We collect thousands of well-composed portrait photographs to learn human-scenery aesthetic composition rules. In addition, we define a set of negative rules to exclude undesirable compositions. Recommendation results are achieved by combining the first learned positive rule with our proposed negative rules. We implement the proposed system on an Android platform in a smartphone. The system demonstrates its efficacy by producing well-composed souvenir photos.
Extreme Adaptation for Personalized Neural Machine Translation
(2018)

Every person speaks or writes their own flavor of their native language, influenced by a number of factors: the content they tend to talk about, their gender, their social status, or their geographical origin. When attempting to perform Machine Translation (MT), these variations have a significant effect on how the system should perform translation, but this is not captured well by standard one-sizefits-all models. In this paper, we propose a simple and parameter-efficient adaptation technique that only requires adapting the bias of the output softmax to each particular user of the MT system, either directly or through a factored approximation. Experiments on TED talks in three languages demonstrate improvements in translation accuracy, and better reflection of speaker traits in the target text.
Three-Level Bidirectional Converter for Fuel-Cell/Battery Hybrid Power System
(2010)

A novel three-level (3L) bidirectional converter (BDC) is proposed in this paper. Compared with the traditional BDC, the inductor of the 3L BDC can be reduced significantly so that the dynamic response is greatly improved. Hence, the proposed converter is very suitable for fuel-cell/battery hybrid power systems. In addition, the voltage stress on the switch of the proposed converter is only half of the voltage on the high-voltage side, so it is also suitable for high-voltage applications. The operation principle and the implementation of the control circuit are presented in detail. This paper also proposes a novel bidirectional soft-start control strategy for the BDC. A 1-kW prototype converter is built to verify the theoretical analysis.
Gamut Mapping to Preserve Spatial Luminance Variations
(2000)

A spatial gamut mapping technique is proposed to overcome the shortcomings encountered with standard pointwise gamut mapping algorithms by preserving spatially local luminance variations in the original image. It does so by first processing the image through a standard pointwise gamut mapping algorithm. The difference between the original image luminance Y and gamut mapped image luminance Y’ is calculated. A spatial filter is then applied to this difference signal, and added back to the gamut mapped signal Y’. The filtering operation can result in colors near the gamut boundary being placed outside the gamut, hence a second gamut mapping step is required to move these pixels back into the gamut. Finally, the in-gamut pixels are processed through a color correction function for the output device, and rendered to that device. Psychophysical experiments validate the superior performance of the proposed algorithm, which reduces many of the artifacts arising from standard pointwise techniques.
Intelligent Device-to-Device Communication in the Internet of Things
(2016)

Analogous to the way humans use the Internet, devices will be the main users in the Internet of Things (IoT) ecosystem. Therefore, device-to-device (D2D) communication is expected to be an intrinsic part of the IoT. Devices will communicate with each other autonomously without any centralized control and collaborate to gather, share, and forward information in a multihop manner. The ability to gather relevant information in real time is key to leveraging the value of the IoT as such information will be transformed into intelligence, which will facilitate the creation of an intelligent environment. Ultimately, the quality of the information gathered depends on how smart the devices are. In addition, these communicating devices will operate with different networking standards, may experience intermittent connectivity with each other, and many of them will be resource constrained. These characteristics open up several networking challenges that traditional routing protocols cannot solve. Consequently, devices will require intelligent routing protocols in order to achieve intelligent D2D communication. We present an overview of how intelligent D2D communication can be achieved in the IoT ecosystem. In particular, we focus on how state-of-the-art routing algorithms can achieve intelligent D2D communication in the IoT.
Social trust and social reciprocity based cooperative D2D communications
(2013)

Thanks to the convergence of pervasive mobile communications and fast-growing online social networking, mobile social networking is penetrating into our everyday life. Aiming to develop a systematic understanding of the interplay between social structure and mobile communications, in this paper we exploit social ties in human social networks to enhance cooperative device-to-device communications. Specifically, as hand-held devices are carried by human beings, we leverage two key social phenomena, namely social trust and social reciprocity, to promote efficient cooperation among devices. With this insight, we develop a coalitional game theoretic framework to devise social-tie based cooperation strategies for device-to-device communications. We also develop a network assisted relay selection mechanism to implement the coalitional game solution, and show that the mechanism is immune to group deviations, individually rational, and truthful. We evaluate the performance of the mechanism by using real social data traces. Numerical results show that the proposed mechanism can achieve up-to 122% performance gain over the case without D2D cooperation.